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Measuring dealer inventories and impact of higher order Greek exposure: an implied volatility modelling approach
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The purpose of this work is to theoretically model and empirically quantify the validity of four distinct trade classification methodologies for estimating market makers (MM) inventory and sub-sequent effect of higher order in explaining daily return variance. It begins by presenting a novel approach to trade classification based on modeling the implied volatility sur-face. This approach aims to be superior to current quote and open-interest based methods in that it does not require the use of quote data and by being a more dynamic way of classifying investor positioning. empirically test these models’ accuracy, we first recreate and aggregate the MM greeks exposure across the proposed models and then regress the changes in inventory to changes in the underlying price. The empirical analysis performed over the period from 1stJanuary 2019 to 31stDecember2019 across five different stocks and one index reveal two important findings: (1) While the volatility model is preferable in terms of trade side classification, it falls short when used to estimate market maker inventory; and (2) The inclusion of vega, vanna and volga hedging effects turn outto be highly significant for explaining daily return variance. Quantitatively, accounting for these higher order greek effects results in a 66.04% increase in explanatory power when compared to merely accounting for gamma effects.
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Market maker inventory Options greeks Dynamic hedging Trade classification Volatility surface modelling